OpenRaiser/Intern-Atlas huggingface dataset:
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Venue: arXiv cs.AI 2016
Year: 2016

Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific models or only on parts of general models. Consequently, a system that can intelligently apply these inference algorithms to different parts of a model for fast reasoning is highly desirable. We introduce a new framework called structured factored inference (SFI) that provides the foundation for such a system. Using models encoded in a probabilistic programming language, SFI provides a sound means to decompose a model into sub-models, apply an inference algorithm to each sub-model, and combine the resulting information to answer a query. Our results show that SFI is nearly as accurate as exact inference yet retains the benefits of approximate inference methods.
Face Detection with the Faster R-CNN
Venue: arXiv cs.CV 2016
Year: 2016

The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks. By training a Faster R-CNN model on the large scale WIDER face dataset, we report state-of-the-art results on two widely used face detection benchmarks, FDDB and the recently released IJB-A.
Firm Growth and Innovation in the ERP Industry: A Systems Thinking Approach
Venue: arXiv cs.CY 2016
Year: 2016

Achievement and sustenance of growth are essential themes in organizational literature. In our paper, we develop models using systems thinking approach to understand how firms achieve and sustain growth in a technology-intensive product domain. We augment these to explain the possible impact of a disruptive technological innovation. We use enterprise software industry as the context where SAP has been acknowledged as the market leader. We find that product differentiation and learning effects helped SAP establish itself, and this growth was further sustained through networks and complementors. Introducing cloud computing as the disruptive innovation, we explain its impact on a firm. Analysis reveals that for the next wave of growth to occur, and to tap into newer markets, it would be imperative for SAP to create attractive cloud based offerings. We also discuss how the model can be enhanced by considering competitor dynamics.
A Conceptual Framework for Accountability in Cloud Computing Service Provision
Venue: arXiv cs.CY 2016
Year: 2016

This paper uses a comprehensive review of the academic and professional literature in relation to accountability in the area of cloud computing service provision. It identifies four key conceptual factors that are necessary for an organisation to be considered as accountable. The four factors were found to be: responsibility, assurance, transparency and remediation. A key finding of the paper is that in order to be considered as an accountable cloud service provider, all four factors need to be implemented and be demonstrable by the organisation.
A framework for detecting fraudulent activities in edo state tax collection system using investigative data mining
Venue: arXiv cs.CY 2016
Year: 2016

The Inland Revenue Services is overwhelmed with gigabyte of disk capacity containing data about tax payers in the state. The data stored on the database increases in size at an alarming rate. This has resulted in a data rich but information poor situation where there is a widening gap between the explosive growth of data and its types, and the ability to analyze and interpret it effectively, hence the need for a new generation of automated and intelligent tools and techniques known as investigative data mining, to look for patterns in data. These patterns can lead to new insights, competitive advantages for business, and tangible benefits for the State Revenue services. This research work focuses on designing effective fraud detection and deterring architecture using investigative data mining technique. The proposed system architecture is designed to reason using Artificial Neural Network and Machine learning algorithm in order to detect and deter fraudulent activities. We recommend that the architectural framework be developed using Object Oriented Programming and Agent Oriented Programming Languages.
Nested Weighted Limit-Average Automata of Bounded Width
Venue: arXiv cs.FL 2016
Year: 2016

While weighted automata provide a natural framework to express quantitative properties, many basic properties like average response time cannot be expressed with weighted automata. Nested weighted automata extend weighted automata and consist of a master automaton and a set of slave automata that are invoked by the master automaton. Nested weighted automata are strictly more expressive than weighted automata (e.g., average response time can be expressed with nested weighted automata), but the basic decision questions have higher complexity (e.g., for deterministic automata, the emptiness question for nested weighted automata is PSPACE-hard, whereas the corresponding complexity for weighted automata is PTIME). We consider a natural subclass of nested weighted automata where at any point at most a bounded number k of slave automata can be active. We focus on automata whose master value function is the limit average. We show that these nested weighted automata with bounded width are strictly more expressive than weighted automata (e.g., average response time with no overlapping requests can be expressed with bound k=1, but not with non-nested weighted automata). We show that the complexity of the basic decision problems (i.e., emptiness and universality) for the subclass with k constant matches the complexity for weighted automata. Moreover, when k is part of the input given in unary we establish PSPACE-completeness.
Training Recurrent Answering Units with Joint Loss Minimization for VQA
Venue: arXiv cs.CV 2016
Year: 2016

We propose a novel algorithm for visual question answering based on a recurrent deep neural network, where every module in the network corresponds to a complete answering unit with attention mechanism by itself. The network is optimized by minimizing loss aggregated from all the units, which share model parameters while receiving different information to compute attention probability. For training, our model attends to a region within image feature map, updates its memory based on the question and attended image feature, and answers the question based on its memory state. This procedure is performed to compute loss in each step. The motivation of this approach is our observation that multi-step inferences are often required to answer questions while each problem may have a unique desirable number of steps, which is difficult to identify in practice. Hence, we always make the first unit in the network solve problems, but allow it to learn the knowledge from the rest of units by backpropagation unless it degrades the model. To implement this idea, we early-stop training each unit as soon as it starts to overfit. Note that, since more complex models tend to overfit on easier questions quickly, the last answering unit in the unfolded recurrent neural network is typically killed first while the first one remains last. We make a single-step prediction for a new question using the shared model. This strategy works better than the other options within our framework since the selected model is trained effectively from all units without overfitting. The proposed algorithm outperforms other multi-step attention based approaches using a single step prediction in VQA dataset.
Store Location Selection via Mining Search Query Logs of Baidu Maps
Venue: arXiv cs.AI 2016
Year: 2016

Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, which is very time consuming, and analytic models based on census data, which are un- able to adapt to the dynamic market. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data-driven prediction models for business store placement. In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps. D3SP first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and sup- ply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also learn to rank models to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiments results demonstrate the effectiveness of our methods. D3SP as the core function for store placement has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.
Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets
Venue: arXiv cs.CV 2016
Year: 2016

Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this paper we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in future studies, to computationally estimate the density distribution of gold nanoparticles in natural rubber samples and to predict reduction kinetics of gold nanoparticles at different time periods.
New Permutation Trinomials From Niho Exponents over Finite Fields with Even Characteristic
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, a class of permutation trinomials of Niho type over finite fields with even characteristic is further investigated. New permutation trinomials from Niho exponents are obtained from linear fractional polynomials over finite fields, and it is shown that the presented results are the generalizations of some earlier works.
Engaging Learning Analytics in MOOCS: the good, the bad, and the ugly
Venue: arXiv cs.CY 2016
Year: 2016

Learning Analytics is an emerging field in the vast areas of Educational Technology and Technology Enhanced Learning (TEL). It provides tools and techniques that offer researchers the ability to analyze, study, and benchmark institutions, learners and teachers as well as online learning environments such as MOOCs. Massive Open Online Courses (MOOCs) are considered to be a very active and an innovative form of bringing educational content to a broad community. Due to the reasons of being free and accessible to the public, MOOCs attracted a large number of heterogeneous learners who differ in education level, gender, and age. However, there are pressing demands to adjust the quality of the hosted courses, as well as controlling the high dropout ratio and the lack of interaction. With the help of Learning Analytics, it is possible to contain such issues. In this publication, we discuss the principles of engaging Learning Analytics in MOOCs learning environments and review its potential and capabilities (the good), constraints (the bad), and fallacy analytics (the ugly) based on our experience in last years.
Evidential Label Propagation Algorithm for Graphs
Venue: arXiv cs.AI 2016
Year: 2016

Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing size of social networks in real world, community detection approaches should be fast and accurate. The Label Propagation Algorithm (LPA) is known to be one of the near-linear solutions and benefits of easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we extend the update rule and propagation criterion of LPA in the framework of belief functions. A new community detection approach, called Evidential Label Propagation (ELP), is proposed as an enhanced version of conventional LPA. The node influence is first defined to guide the propagation process. The plausibility is used to determine the domain label of each node. The update order of nodes is discussed to improve the robustness of the method. ELP algorithm will converge after the domain labels of all the nodes become unchanged. The mass assignments are calculated finally as memberships of nodes. The overlapping nodes and outliers can be detected simultaneously through the proposed method. The experimental results demonstrate the effectiveness of ELP.
Mathematical Modeling of Dynamics for Partially Filled Shells of Revolution
Venue: arXiv cs.CE 2016
Year: 2016

In this work we study the dynamic behaviour of compound shells of revolution partially filled with an ideal incompressible fluid based on boundary-value problems. New analytical mathematical model with corresponding discrete scheme for the elastic displacements and the dynamic liquid pressure is developed. The discrete scheme is based on the method of discrete singularities. A code to perform the numerical analysis is developed. Comprehensive benchmarking of the obtained results against other methods is done and good agreement is observed. The convergence of the proposed numerical method is demonstrated. One of the advantages of this new model is that the initial 3D problem is analytically reduced to a 1D integral equation. Moreover, it can handle the behaviour of the pressure in the vicinity of the nodes explicitly and the computational technique used has a quick convergence requiring a negligible amount of CPU time.
Evaluating Co-Authorship Networks in Author Name Disambiguation for Common Names
Venue: arXiv cs.DL 2016
Year: 2016

With the increasing size of digital libraries it has become a challenge to identify author names correctly. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem). This paper focuses on homonym names in the computer science bibliography DBLP. The goal of this study is to evaluate a method which uses co-authorship networks and analyze the effect of common names on it. For this purpose we clustered the publications of authors with the same name and measured the effectiveness of the method against a gold standard of manually assigned DBLP records. The results show that despite the good performance of implemented method for most names, we should optimize for common names. Hence community detection was employed to optimize the method. Results prove that the applied method improves the performance for these names.
Drawing Planar Graphs with Many Collinear Vertices
Venue: arXiv cs.CG 2016
Year: 2016

Consider the following problem: Given a planar graph $G$, what is the maximum number $p$ such that $G$ has a planar straight-line drawing with $p$ collinear vertices? This problem resides at the core of several graph drawing problems, including universal point subsets, untangling, and column planarity. The following results are known for it: Every $n$-vertex planar graph has a planar straight-line drawing with $Ω(\sqrt{n})$ collinear vertices; for every $n$, there is an $n$-vertex planar graph whose every planar straight-line drawing has $O(n^σ)$ collinear vertices, where $σ<0.986$; every $n$-vertex planar graph of treewidth at most two has a planar straight-line drawing with $Θ(n)$ collinear vertices. We extend the linear bound to planar graphs of treewidth at most three and to triconnected cubic planar graphs. This (partially) answers two open problems posed by Ravsky and Verbitsky [WG 2011:295--306]. Similar results are not possible for all bounded treewidth planar graphs or for all bounded degree planar graphs. For planar graphs of treewidth at most three, our results also imply asymptotically tight bounds for all of the other above mentioned graph drawing problems.
FM-index of Alignment with Gaps
Venue: arXiv cs.DS 2016
Year: 2016

Recently, a compressed index for similar strings, called the FM-index of alignment (FMA), has been proposed with the functionalities of pattern search and random access. The FMA is quite efficient in space requirement and pattern search time, but it is applicable only for an alignment of similar strings without gaps. In this paper we propose the FM-index of alignment with gaps, a realistic index for similar strings, which allows gaps in their alignment. For this, we design a new version of the suffix array of alignment by using alignment transformation and a new definition of the alignment-suffix. The new suffix array of alignment enables us to support the LF-mapping and backward search, the key functionalities of the FM-index, regardless of gap existence in the alignment. We experimentally compared our index with RLCSA due to Makinen et al. on 100 genome sequences from the 1000 Genomes Project. The index size of our index is less than one third of that of RLCSA.
How marketing vocabulary was evolving from 2005 to 2014? An illustrative application of statistical methods on text mining
Venue: arXiv cs.DL 2016
Year: 2016

Here a collection of 1169 abstracts, which corresponds to articles that the Journal of Marketing Research has published from 2005 to 2014, are analysed under a novel approach. We apply several statistical methods, such as Principal Components Analysis and Correspondence Analysis to identify the way Marketing vocabulary is evolving. Similarly those articles that introduce new vocabulary are identified and the preferred words by authors are also detected. In order to provide an easy-to-understand explanation, we provide our results graphically. A word-cloud with the most frequent words is given first. Secondly abstracts-words are represented on the factorial plane. Finally one representation of word-years allows us to detect changes on the vocabulary through the passing of time.
A Chomsky-Schützenberger representation for weighted multiple context-free languages
Venue: arXiv cs.FL 2016
Year: 2016

We prove a Chomsky-Schützenberger representation theorem for multiple context-free languages weighted over complete commutative strong bimonoids.
Micro-interventions in urban transport from pattern discovery on the flow of passengers and on the bus network
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.
Active Discriminative Text Representation Learning
Venue: arXiv cs.CL 2016
Year: 2016

We propose a new active learning (AL) method for text classification with convolutional neural networks (CNNs). In AL, one selects the instances to be manually labeled with the aim of maximizing model performance with minimal effort. Neural models capitalize on word embeddings as representations (features), tuning these to the task at hand. We argue that AL strategies for multi-layered neural models should focus on selecting instances that most affect the embedding space (i.e., induce discriminative word representations). This is in contrast to traditional AL approaches (e.g., entropy-based uncertainty sampling), which specify higher level objectives. We propose a simple approach for sentence classification that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings. We extend this approach to document classification by jointly considering: (1) the expected changes to the constituent word representations; and (2) the model's current overall uncertainty regarding the instance. The relative emphasis placed on these criteria is governed by a stochastic process that favors selecting instances likely to improve representations at the outset of learning, and then shifts toward general uncertainty sampling as AL progresses. Empirical results show that our method outperforms baseline AL approaches on both sentence and document classification tasks. We also show that, as expected, the method quickly learns discriminative word embeddings. To the best of our knowledge, this is the first work on AL addressing neural models for text classification.
On degeneration of tensors and algebras
Venue: arXiv cs.CC 2016
Year: 2016

An important building block in all current asymptotically fast algorithms for matrix multiplication are tensors with low border rank, that is, tensors whose border rank is equal or very close to their size. To find new asymptotically fast algorithms for matrix multiplication, it seems to be important to understand those tensors whose border rank is as small as possible, so called tensors of minimal border rank. We investigate the connection between degenerations of associative algebras and degenerations of their structure tensors in the sense of Strassen. It allows us to describe an open subset of $n \times n \times n$ tensors of minimal border rank in terms of smoothability of commutative algebras. We describe the smoothable algebra associated to the Coppersmith-Winograd tensor and prove a lower bound for the border rank of the tensor used in the "easy construction" of Coppersmith and Winograd.
How hot is .brussels? Impact of the uptake of the .brussels top-level domain name extension
Venue: arXiv cs.CY 2016
Year: 2016

The opening up of the top-level domain name market in 2012 has offered new perspectives for companies, administrations and individuals to include a geographic component within the domain name of their website. Little to no research has been carried out since then to analyse the uptake of the new top-level domain names (TLDN). Based on the specific case of the TLDN .brussels, this article proposes an empirical study of how the opening up of the top-level domain name market actually impacts registration practices. By making use of freely available software tools such as OpenRefine and Natural Language Processing (NLP) methods, the entire corpus of the .brussels domain names (6300) was analysed from a quantitative perspective. Based on a statistically representative sample, a qualitative interpretation allowed for a more fine-grained analysis of how the new TLDN is being used in practice. By doing so, the article gives a detailed insight into the impact of the recent changes of the rules concerning domain name registration. Researchers, policy makers, investors and anyone who cares about the Brussels identity in the digital realm can gain through this analysis a better understanding of the state of play of the .brussels TLDN.